\section{Theoretical Justification of the Open Gover\-nance \mbox{} Maturity Matrix}
\label{sec:justification}

We will go though each of the theoretical areas presented in chapter \ref{chap:theory} and discuss to which extent our grounded findings live up to this theory. This will function as a \textit{verification} our findings and the Open Governance Maturity Matrix. As to our grounded theory approach we are not obliged to \textit{validate} our model but this section will enable us to indicate how it can be validated in the future.

\subsubsection*{Systems Theory}

One of the points we deduced primarily from \cite{ACKOFF} and \cite{LUHMANN} was that a complex system such as a society -- or more concretely a city -- needs to be broken down in smaller better defined, tangible subsystems. These subsystems do not necessarily have to have a well-defined set of actors and relations but they need to be defined and conceptualised in ways that enable them to be considered as purposeful and ideal-seeking. If such a system can be defined and developed then it is optimisable. One can relate to the concept of maturity and claim that a true ideal-seeking system corresponds to the highest possible collective maturity level of an open data project in a city context. Let us explain how the Open Governance Maturity Matrix can contribute to the determination of subsystems, collection of system reactions and responses, and system optimisation that strives for ideal-seeking conditions which again catalyse silos breakdown and holistic open data collaboration. \bigskip

\noindent Stage 1 in the maturity matrix requires a potential project to have a clear vision, and so it is immediately initiating a subsystem formation, which does not have to be clear yet. As the project moves on, the objectives and constrains can be addressed and new norms can evolve because the actors increasingly get more aware of their specific role (Stages 2-8).

As explained in the findings, section \ref{sec:findings}, one incentive to participate in open data collaboration is quick, short-term wins, and this issue is addressed in the dependency D1, where incentives have to be found and aligned among the actors for the process to move on. \marginpar{\footnotesize Example of a system \textbf{\textit{reaction}}: If opening up certain data in a certain way with out a doubt causes another actor to open up certain data. Other reactions will then occur, such as rebound effects.} This will catalyse specific open data collaboration incentives and the subsystem will gradually become more well-defined because possible system reactions and responses will be identified by encouraging actors to communicate and compromise so everyone thinks the value proposition and incentives counterbalance the risks. When incentives and obstacles for collaboration and communication are addressed, our maturity matrix will automatically lead to an evaluation of these and thus, as the system gets more well-defined, it also leads to system optimization. An optimization that has an \textit{idealistic} goal, which is pursued systematically and in interrelated steps, as \cite{ACKOFF} prescribes for ideal-seeking systems. \marginpar{\footnotesize Example of a system \textbf{\textit{response}}: When funding is a vital catalyst to actor participation but not sufficient.}

\subsubsection*{Ecosystems Theory}

A business ecosystem is another way to describe the wanted purposive, ideal-seeking system. The Open Governance Maturity Matrix makes sure that a business ecosystem is created around specific visions and subsystems within the city. The maturity matrix is addressing the necessity for public/private ecosystem leadership by means of a mediator cluster organisation. Through the eight stages of maturity the four stages of business ecosystems given by \cite{MOORE} are also traversed: first we have birth, then there is an expansion, and at last the project will reach a self-renewal stage where the collective ecosystem mindset is agile and constantly seeking synergies to overcome external threats.

It is relevant to discuss how the stage of business ecosystem leadership is handled in our maturity matrix. We have identified the initial leader to be a cross-industry cluster organisation like Copenhagen Capacity or the CLEAN cluster, which represent many different interests and understand the need for a holistic approach. They are the obvious keystones and regulators of ecosystem health. But business ecosystems are according to \cite{MOORE} in constant competition with other ecosystems and this is not necessarily the case for open data ecosystems within smart cities because the public sector is an inevitable actor. And so, if leadership is given to a private actor it might be more effective in the short-term, but the incentives to do holistic decisions might disintegrate unless a high collective maturity is reached. Therefore we argue that cluster organisations should maintain their ecosystem leadership as long as possible or until the current institutions are properly redesigned.

\subsubsection*{Institutional Theory}

Institutions include the norms, policies, constitutions, and behaviour surrounding the ecosystem. One of the underlying purposes of the Open Governance Maturity Matrix is to design and evolve institutions that are beneficial for open data collaboration. The maturity matrix is built on many of the ingredients to proper institutional design given by \cite{JOOP}. An important part of institutional design is stakeholder identification, which we address in the maturity matrix. 

Furthermore, their meta-model includes development of requirements and formulation of objectives and constraints, which we have translated into an overall vision and obstacles and incentives for open data collaboration.

They emphasize the importance of performance indicators when doing institutional design. Our maturity matrix does not mandate performance indicator considerations but can function as a generic performance indicator in itself. Even though Boyd-Cohen writes that performance indicators must not and cannot be generic on a city level, it is possible to use our maturity matrix on a subsystem level. This, of course, does not prevent performance indicators to be used along with the maturing process.

\subsubsection*{Open Innovation Theory}

Open data collaboration is inevitably dependent on a strong platform for open innovation where ideas can be collected and refined and reach the socio-technical system \citep{SCHAFFERS2011}. One of the goals of open data is to expose information in an agreed, standardized way for example in an open API, which is free to be used by companies and entrepreneurs. The Open Governance Maturity Matrix ensures that these standards are agreed upon by relevant actors. At the same time it provides collective incentives for these actors to break down their business silos and find synergies between their business domains - this can also be considered as open innovation where purposive inflows and outflows result in projects with innovative outcomes. \bigskip

\noindent When the project reaches a high maturity level it is ready to integrate with other projects which catalyse new forms of open innovation. A more in-depth discussion of the possibilities for a systems integration process will come in subsection \ref{subsec:integration}. \cite{CHESBROUGH} emphasized the importance of proper funding of open innovation and this issue will be covered more in depth in subsection \ref{subsec:funding}. \bigskip

\noindent Open innovation is further catalysed because the maturity matrix assembles the four different types of organizations needed for innovation generation: Innovation explorers will typically be public institutions, merchants will primarily consist of private actors looking for economic benefits but will also be presented by public actors, architects will be the supplier of complex technologies, and the initial missionary will be the keystone cluster organisation. The Open Governance Maturity Matrix generates a platform for all these types of actors to collaborate and it also provides incentives for them to participate.